A dual-attention feature fusion network for imbalanced fault diagnosis with two-stream hybrid generated data

Chenze Wang (), Han Wang () and Min Liu ()
Additional contact information
Chenze Wang: Tongji University
Han Wang: Tongji University
Min Liu: Tongji University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 15, 1707-1719

Abstract: Abstract Deep learning-based fault diagnosis models achieve great success with sufficient balanced data, but the imbalanced dataset in real industrial scenarios will seriously affect the performance of various popular deep learning models. Data generation-based strategy provides a solution by expanding the number of minority samples. However, many data-generation methods cannot generate high-quality samples when the imbalanced ratio is high. To address these problems, a dual-attention feature fusion network (DAFFN) with two-stream hybrid-generated data is proposed. First, the two-stream hybrid generator including a generative model and an oversampling technique is adopted to generate minority fault data. Then, the convolutional neural network is used to extract features from hybrid-generated data. In particular, a feature fusion network with a dual-attention mechanism, i.e., a channel attention mechanism and a layer attention mechanism are designed to learn channel-level and layer-level weights of the features. Extensive results on two bearing datasets indicate that the proposed framework achieves outstanding performance in various high imbalanced-ratio cases.

Keywords: Fault diagnosis; Imbalanced dataset; Hybrid generated data; Dual-attention mechanism; Feature fusion network (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02131-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02131-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02131-2

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().